Advanced Information and Knowledge Processing

Robust Representation for Data Analytics

Models and Applications

Authors: Li, Sheng, Fu, Yun

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  • Enriches understanding of robust feature representations
  • Explains how to develop robust data mining models
  • Reinforces robust representation principles with real-world practice
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eBook 96,29 €
price for Spain (gross)
  • ISBN 978-3-319-60176-2
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Hardcover 124,79 €
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Softcover 124,79 €
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About this book

This book introduces the concepts and models of robust representation learning, and provides a set of solutions to deal with real-world data analytics tasks, such as clustering, classification, time series modeling, outlier detection, collaborative filtering, community detection, etc. Three types of robust feature representations are developed, which extend the understanding of graph, subspace, and dictionary.

Leveraging the theory of low-rank and sparse modeling, the authors develop robust feature representations under various learning paradigms, including unsupervised learning, supervised learning, semi-supervised learning, multi-view learning, transfer learning, and deep learning. Robust Representations for Data Analytics covers a wide range of applications in the research fields of big data, human-centered computing, pattern recognition, digital marketing, web mining, and computer vision.

Table of contents (10 chapters)

Table of contents (10 chapters)

Buy this book

eBook 96,29 €
price for Spain (gross)
  • ISBN 978-3-319-60176-2
  • Digitally watermarked, DRM-free
  • Included format: PDF, EPUB
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase Institutional customers should get in touch with their account manager
Hardcover 124,79 €
price for Spain (gross)
Softcover 124,79 €
price for Spain (gross)
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Bibliographic Information

Bibliographic Information
Book Title
Robust Representation for Data Analytics
Book Subtitle
Models and Applications
Authors
Series Title
Advanced Information and Knowledge Processing
Copyright
2017
Publisher
Springer International Publishing
Copyright Holder
Springer International Publishing AG, part of Springer Nature
eBook ISBN
978-3-319-60176-2
DOI
10.1007/978-3-319-60176-2
Hardcover ISBN
978-3-319-60175-5
Softcover ISBN
978-3-319-86796-0
Series ISSN
1610-3947
Edition Number
1
Number of Pages
XI, 224
Number of Illustrations
3 b/w illustrations, 49 illustrations in colour
Topics